Asking ‘what it does’ rather than ‘what it is’: the invisibility and opportunity of Taiwan’s role on the global health stage

In 2017, some twenty Taiwanese students from diverse disciplinary backgrounds formed a study group: medicine, epidemiology, law, sociology, politics, and geography. Its overall objective is to understand better what has been referred to as ‘global health’. For that, we have been thinking of a difficult yet crucial question: what is global health, and why should we, as Taiwanese citizens, need to study it? In this article, we reflect on our motivations and discussions. We began by reading the text ‘Governing Global Health: Who Runs the World and Why?’, which compares the activities of different global health actors – intergovernmental and nongovernmental – and their relationships with nations states. Building on this approach, in this article we will draw on the limitations and opportunities for Taiwan to take part in the contemporary global health system

Lexical Retrieval Hypothesis in Multimodal Context

Multimodal corpora have become an essential language resource for language science and grounded natural language processing (NLP) systems due to the growing need to understand and interpret human communication across various channels. In this paper, we first present our efforts in building the first Multimodal Corpus for Languages in Taiwan (MultiMoco). Based on the corpus, we conduct a case study investigating the Lexical Retrieval Hypothesis (LRH), specifically examining whether the hand gestures co-occurring with speech constants facilitate lexical retrieval or serve other discourse functions. With detailed annotations on eight parliamentary interpellations in Taiwan Mandarin, we explore the co-occurrence between speech constants and non-verbal features (i.e., head movement, face movement, hand gesture, and function of hand gesture). Our findings suggest that while hand gestures do serve as facilitators for lexical retrieval in some cases, they also serve the purpose of information emphasis. This study highlights the potential of the MultiMoco Corpus to provide an important resource for in-depth analysis and further research in multimodal communication studies

Exploring Affordance and Situated Meaning in Image Captions: A Multimodal Analysis

This paper explores the grounding issue regarding multimodal semantic representation from a computational cognitive-linguistic view. We annotate images from the Flickr30k dataset with five perceptual properties: Affordance, Perceptual Salience, Object Number, Gaze Cueing, and Ecological Niche Association (ENA), and examine their association with textual elements in the image captions. Our findings reveal that images with Gibsonian affordance show a higher frequency of captions containing 'holding-verbs' and 'container-nouns' compared to images displaying telic affordance. Perceptual Salience, Object Number, and ENA are also associated with the choice of linguistic expressions. Our study demonstrates that comprehensive understanding of objects or events requires cognitive attention, semantic nuances in language, and integration across multiple modalities. We highlight the vital importance of situated meaning and affordance grounding in natural language understanding, with the potential to advance human-like interpretation in various scenarios.Comment: 10 pages, 9 figure

Topological susceptibility in 2+1 flavors lattice QCD with domain-wall fermions

We measure the topological charge and its fluctuation for the gauge configurations generated by the RBC and UKQCD Collaborations using 2+1 flavors of domain-wall fermions on the 16^3 x 32 lattice (L \simeq 2 fm) with length 16 in the fifth dimension at inverse lattice spacing a^{-1} \simeq 1.62 GeV. From the spectral flow of the Hermitian operator H_w (2 + \gamma_5 H_w)^{-1}, we obtain the topological charge Q_t of each gauge configuration in the three ensembles with light sea quark masses m_q a = 0.01, 0.02, and 0.03, and with the strange quark mass fixed at m_s a = 0.04. From our result of Q_t, we compute the topological susceptibilty \chi_t = /volume. In the small m_q regime, our result of \chi_t agrees with the chiral effective theory. Using the formula \chi_t = \Sigma / (m_u^{-1} + m_d^{-1} + m_s^{-1}) by Leutwyler-Smilga, we obtain the chiral condensate \Sigma^MSbar(2 GeV) = [259(6)(9) MeV]^3.Comment: 9 pages, 3 EPS figure

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.Peer reviewe

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.</p